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Welcome to the Intel Retail GitHub Organization!

The Retail AI Suite is part of Open Edge Platform.

Intel Retail is a collection of pipelines, tools, and resources designed to accelerate hardware decisions for Retail AI workloads at the edge, featuring use cases such as self-checkout and loss prevention.

Intel’s Open Edge Platform is a secure and optimized open platform for delivering scalable edge solutions. Retail constitutes one of its AI Software Suites, offering several sample applications that showcase the use of platform's software stack. The following are the main components of the suite:

  • Order Accuracy Pipeline System is an open-source reference implementation for building and deploying video analytics pipelines for retail order accuracy in Quick Service Restaurant (QSR) use cases. It leverages Intel® hardware and software, GStreamer, and OpenVINO™ to enable scalable, real-time object detection and classification at the edge.
  • Loss Prevention Pipeline System is an open-source reference implementation for building and deploying video analytics pipelines for retail loss prevention use cases. It leverages Intel® hardware and software, GStreamer, and OpenVINO™ to enable scalable, real-time object detection and classification at the edge.
  • Performance Tools is a dockerized performance tool suite for benchmarking a use case.
  • Automated Self-Checkout Reference Package is a sample design of a self-checkout system, optimized for Intel hardware and the Open Edge Platform ecosystem.

Unless otherwise noted, the Retail repositories are released under the Apache 2.0 license.

Access the documentation

User documentation of the main components is available at the official Open Edge Platform documentation pages.

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Popular repositories Loading

  1. automated-self-checkout automated-self-checkout Public

    The Intel® Automated Self-Checkout Reference Package provides critical components required to build and deploy a self-checkout use case using Intel® hardware, software, and other open-source compon…

    Jupyter Notebook 34 62

  2. rtsf-at-checkout-reference-design rtsf-at-checkout-reference-design Public archive

    Detect loss at self-checkout by seamlessly connecting different sensor devices, including weight scale sensors, cameras, and RFIDs.

    Go 12 18

  3. automated-vending automated-vending Public archive

    Deploy sensor fusion technology for an automated checkout that enables real-time insight about the products consumers are buying using the EdgeX Foundry* extensible framework.

    Go 11 15

  4. software-vsync-modulation-sample software-vsync-modulation-sample Public

    A SW Library for Digital Signage that provides references on how to modify MMIO PLL registers to slow/speed up VSYNC timings on a system to achieve synchronization. For documentation click on the l…

    C++ 10 6

  5. loss-prevention loss-prevention Public

    The Loss Prevention Pipeline System is an open-source reference implementation for building and deploying video analytics pipelines for retail loss prevention use cases. It leverages Intel® hardwar…

    Python 8 23

  6. retail-use-cases retail-use-cases Public

    Retail based use case profiles.

    Python 4 20

Repositories

Showing 10 of 15 repositories
  • digital-signage Public

    Sample app to showcase usage of DL Streamer Pipeline Server for object detection and OpenVINO GenAI for advertisement generation (text-to-image models) to upsell or cross sell.

    intel-retail/digital-signage's past year of commit activity
    Python 1 Apache-2.0 5 1 9 Updated Sep 9, 2026
  • intel-retail/voice-enabled-interactions's past year of commit activity
    Python 2 9 37 1 Updated Sep 9, 2026
  • order-accuracy Public

    The Order Accuracy Pipeline System is an open-source reference implementation for building and deploying video analytics pipelines for retail order accuracy in Quick Servce Restaurant(QSR) use cases. It leverages Intel® hardware and software, GStreamer, and OpenVINO™ to enable scalable, real-time object detection and classification at the edge.

    intel-retail/order-accuracy's past year of commit activity
    Python 2 Apache-2.0 16 11 18 Updated Sep 7, 2026
  • intel-retail/storewide-loss-prevention's past year of commit activity
    Python 1 11 10 6 Updated Sep 7, 2026
  • intel-retail/qsr-store-pos-agent's past year of commit activity
    TypeScript 0 Apache-2.0 1 0 0 Updated Sep 5, 2026
  • documentation Public

    Documentation and Requirements for Intel Retail Organization

    intel-retail/documentation's past year of commit activity
    HTML 1 Apache-2.0 22 5 4 Updated Sep 4, 2026
  • loss-prevention Public

    The Loss Prevention Pipeline System is an open-source reference implementation for building and deploying video analytics pipelines for retail loss prevention use cases. It leverages Intel® hardware and software, GStreamer, and OpenVINO™ to enable scalable, real-time object detection and classification at the edge.

    intel-retail/loss-prevention's past year of commit activity
    Python 8 Apache-2.0 23 33 14 Updated Sep 4, 2026
  • performance-tools Public

    Dockerized performance tool suite for benchmarking a use case.

    intel-retail/performance-tools's past year of commit activity
    Python 2 Apache-2.0 28 13 8 Updated Aug 14, 2026
  • automated-self-checkout Public

    The Intel® Automated Self-Checkout Reference Package provides critical components required to build and deploy a self-checkout use case using Intel® hardware, software, and other open-source components.

    intel-retail/automated-self-checkout's past year of commit activity
    Jupyter Notebook 34 Apache-2.0 62 28 44 Updated Jul 14, 2026
  • retail-use-cases Public

    Retail based use case profiles.

    intel-retail/retail-use-cases's past year of commit activity
    Python 4 Apache-2.0 20 30 1 Updated Jun 4, 2026

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